World

Column

Total Cases

Column

Total Deaths

Cases

Column

Total Cases

Column

New Cases

Column

Var. (%)

Deaths

Column

Total Deaths

Column

New Deaths

Column

Var. (%)

Cases

Column

Total Cases

Column

New Cases

Column

Var. (%)

Deaths

Column

Total Deaths

Column

New Deaths

Column

Var. (%)

Cases

Column

Total Cases

Column

New Cases

Column

Var. (%)

Deaths

Column

Total Deaths

Column

New Deaths

Column

Var. (%)

Total Cases by Country Group

Column

Asia Casos. China: 82809, Japan: 4257, Singapore: 1623, Korea, South: 10384

Europe Casos. Germany: 113296, Italy: 139422, France: 113959, Spain: 148220

America Casos. Mexico: 2785, Brazil: 16170, US: 429052

Column

Asia Mortes. China: 3337, Japan: 93, Singapore: 6, Korea, South: 200

Europe Mortes. Germany: 2349, Italy: 17669, France: 10887, Spain: 14792

America Mortes. Mexico: 141, Brazil: 819, US: 14695

# # Test 1 {data-navmenu=‘Figures’} # ======================================================== # # Row {.tabset} # ——————————————————— # # ### America (Cases) # {r, fig.width = 10, fig.height = 5} # # # Data frame with deaths # df = data.frame(coredata(10**(dts[,countriesAmerica] - 1)), index(dts[,countriesAmerica])) # colnames(df) <- c(countriesAmerica, 'date') # # # Data frame with cases # cf = data.frame(coredata(10**(cts[,countriesAmerica] - 1)), index(cts[,countriesAmerica])) # colnames(cf) <- c(countriesAmerica, 'date') # # f1 <- list(family = 'Old Standard TT, serif', size = 14, color = 'black') # # c_axis <- list( # title = "Cases (Log scale)", # titlefont = f1, # showticklabels = TRUE # ) # # x_axis <- list( # title = "Days", # titlefont = f1, # showticklabels = TRUE # ) # # clr <- lcolors[c(1,3,4)] # names(clr) <- countriesAmerica # # fig <- plot_ly(cf, x = ~date) # for (country in countriesAmerica) { # fig <- fig %>% add_trace(y = as.formula(paste0('~', country)), # name = country, # mode = 'markers', # marker = list(color = unname(clr[country]), size = 10)) # } # fig <- fig %>% layout(xaxis = x_axis, yaxis = c_axis) # fig # # # ### America (Deaths) # {r} # tp <- tail(df, 5) # # p <- plot_ly() # for(country in countriesAmerica) { # p <- p %>% add_trace(data = tp, x = ~date, y = as.formula(paste0('~', country)), name = country, type = 'bar', marker = list(color = unname(clr[country]))) # } # # xax <- list( # title = 'Days', # titlefont = f1, # showticklabels = TRUE # ) # # yax <- list( # title = 'Reported deaths', # titlefont = f1, # showticklabels = TRUE # ) # # p %>% layout(xaxis = xax, yaxis = yax) # # # Row {.tabset} # ——————————————————— # # ### Asia (Cases) # {r, fig.width = 10, fig.height = 5} # # # Data frame with deaths # df = data.frame(coredata(10**(dts[,countriesAsia] - 1)), index(dts[,countriesAsia])) # colnames(df) <- c(countriesAsia, 'date') # # # Data frame with cases # cf = data.frame(coredata(10**(cts[,countriesAsia] - 1)), index(cts[,countriesAsia])) # colnames(cf) <- c(countriesAsia, 'date') # # f1 <- list(family = 'Old Standard TT, serif', size = 14, color = 'black') # # c_axis <- list( # title = "Cases", # titlefont = f1, # showticklabels = TRUE # ) # # x_axis <- list( # title = "Days", # titlefont = f1, # showticklabels = TRUE # ) # # clr <- lcolors[c(1,3,4)] # names(clr) <- rev(countriesAsia[1:3]) # # fig <- plot_ly(cf, x = ~date) # for (country in countriesAsia[1:3]) { # fig <- fig %>% add_trace(y = as.formula(paste0('~', country)), # name = country, # mode = 'markers', # marker = list(color = unname(clr[country]), size = 10)) # } # fig <- fig %>% layout(xaxis = x_axis, yaxis = c_axis) # fig # # # ### Asia (Deaths) # {r} # tp <- tail(df, 5) # # p <- plot_ly() # for(country in rev(countriesAsia[1:3])) { # p <- p %>% add_trace(data = tp, x = ~date, y = as.formula(paste0('~', country)), name = country, type = 'bar', marker = list(color = unname(clr[country]))) # } # # xax <- list( # title = 'Days', # titlefont = f1, # showticklabels = TRUE # ) # # yax <- list( # title = 'Reported deaths', # titlefont = f1, # showticklabels = TRUE # ) # # p %>% layout(xaxis = xax, yaxis = yax) #

Comparative After 100 cases

Comparative After 100 cases

Total Cases After Lockdown

Column

Column